Magnetic resonance imaging apparatus, image processing apparatus, and image processing method

ABSTRACT

A magnetic resonance imaging apparatus according to an embodiment includes an MRI system and a processing circuitry. The MRI system includes a receiving coil to receive a magnetic resonance signal. The processing circuitry is configured to generate an image based on the magnetic resonance signal, the image including a plurality of pixels; calculate a feature value corresponding to a signal value of the pixel; correct the feature values based on a sensitivity of the receiving coil; and reduce noise in the image based on distribution of the corrected feature values.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/935,951, filed on Nov. 9, 2015, which is based upon and claims thebenefit of priorities from Japanese Patent Applications No. 2014-228483,filed on Nov. 10, 2014, and No. 2015-217590, filed on Nov. 5, 2015; theentire contents of which are incorporated herein by reference.

FIELD

Embodiments described herein relate generally to a magnetic resonanceimaging apparatus, an image processing apparatus, and an imageprocessing method.

BACKGROUND

In related art, there is a known method of calculating a standarddeviation of temporal change in signal intensity for each pixel of aframe, and determining an average of the calculated standard deviationsof all the pixels as a magnitude of the noise, as a technique forremoving noise in an image. Because an average of all the pixels is usedin such a method, there are cases where it is difficult to properlyremove noise in all the pixels, for example, in the case wheredifference in S/N (SN ratio) occurs among a plurality of pixels includedin the image.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a configuration example of an MRIapparatus according to a first embodiment;

FIG. 2 is a block diagram illustrating a configuration example offunctions included in the MRI apparatus according to the firstembodiment;

FIG. 3 is a flowchart illustrating processing procedures of an imageprocessing method performed by the MRI apparatus according to the firstembodiment;

FIG. 4 is a diagram illustrating an example of a neighborhood R used bya feature vector calculator according to the first embodiment;

FIG. 5 is a diagram illustrating an example of noise model selectionperformed by a model selector according to the first embodiment;

FIG. 6 is a diagram illustrating a setting example of a neighborhood Raccording to a second modification; and

FIG. 7 is a diagram illustrating an example of noise removal accordingto an eleventh modification;

FIG. 8 is a flowchart illustrating processing procedures of noiseremoval according to an eleventh modification;

FIG. 9 is a block diagram illustrating a configuration of an imageprocessing apparatus according to a second embodiment.

DETAILED DESCRIPTION

A magnetic resonance imaging apparatus according to an embodimentincludes an MRI system and a processing circuitry. The MRI systemincludes a receiving coil to receive a magnetic resonance signal. Theprocessing circuitry is configured to generate an image based on themagnetic resonance signal, the image including a plurality of pixels;calculate a feature value corresponding to a signal value of the pixel;correct the feature values based on a sensitivity of the receiving coil;and reduce noise in the image based on distribution of the correctedfeature values.

The following is detailed explanation of embodiments of the MRIapparatus, the image processing apparatus, and the image processingmethod.

First Embodiment

FIG. 1 is a diagram illustrating a configuration example of an MRIapparatus according to a first embodiment. For example, as illustratedin FIG. 1, the MRI apparatus 100 includes a static magnetic field magnet1, a gradient coil 2, a gradient power supply 3, a couch 4, a couchcontroller 5, a transmitting coil 6, a transmitter 7, a receiving coil8, a receiver 9, and a computer system 20.

The static magnetic field magnet 1 is formed in a hollow andsubstantially cylindrical shape (including a shape having an oval crosssection orthogonal to the central axis of a cylinder), and generates auniform static magnetic field in an imaging space formed inside thereof.The static magnetic field magnet 1 is, for example, a permanent magnet,or a superconducting magnet.

The gradient coil 2 is formed in a hollow and substantially cylindricalshape (including a shape having an oval cross section orthogonal to thecentral axis of a cylinder), and disposed inside the static magneticfield magnet 1. Specifically, the gradient coil 2 is formed of acombination of three coils corresponding to respective axes of x, y, andz that are orthogonal to each other. The three coils generate gradientmagnetic fields having magnetic field intensities changing along therespective axes of x, y, and z orthogonal to each other, in the imagingspace, with electric currents that are individually supplied from thegradient power supply 3. The direction of the z axis is set to be thesame as the direction of the magnetic flux of the static magnetic field.

The gradient power supply 3 supplies electric power to the gradient coil2, to cause the gradient coil 2 to generate gradient magnetic fields.Specifically, the gradient power supply 3 individually supplies electriccurrents to each of the three coils included in the gradient coil 2 tocause the three coils to generate gradient magnetic fields along therespective axes of x, y, and z, as appropriate, to generate respectivegradient magnetic fields along a readout direction, a phase encodingdirection, and a slice direction that are orthogonal to each other, inthe imaging space. In the following explanation, a gradient magneticfield along the readout direction is referred to as readout gradientmagnetic field, a gradient magnetic field along the phase encodingdirection is referred to as phase encoding gradient magnetic field, anda gradient magnetic field along the slice direction is referred to asslice gradient magnetic field.

These three directions are used for providing an MR signal with spatialpositional information. Specifically, the readout gradient magneticfield provides a magnetic resonance (MR) signal with positionalinformation of the readout direction, by changing a frequency of the MRsignal in accordance with the position in the readout direction. Thephase encoding gradient magnetic field provides an MR signal withpositional information of the phase encoding direction, by changing aphase of the MR signal in accordance with the position in the phaseencoding direction. The slice gradient magnetic field is used fordetermining the direction, the thickness, and the number of sliceregions, when the imaging region is a slice region. When the imagingregion is a volume region, the slice gradient magnetic field provides anMR signal with positional information along the slice direction, bychanging the phase of the MR signal in accordance with the position inthe slice direction.

The couch 4 includes a couchtop 4 a on which a subject S is placed. Thecouch 4 inserts the couchtop 4 a into the imaging space formed insidethe static magnetic field magnet 1 and the gradient coil 2. For example,the couch 4 is installed such that a longitudinal direction of the couch4 is parallel with the central axis of the static magnetic field magnet1.

The couch controller 5 controls operations of the couch 4. For example,the couch controller 5 controls a driving mechanism included in thecouch 4, to move the couchtop 4 a in the longitudinal direction, thevertical direction, or the lateral direction.

The transmitting coil 6 is formed in a hollow and substantiallycylindrical shape (including a shape having an oval cross sectionorthogonal to the central axis of a cylinder), and disposed inside thegradient coil 2. The transmitting coil 6 applies a radio frequency (RF)magnetic field to the imaging space, with an RF pulse current suppliedfrom the transmitter 7.

The transmitter 7 supplies an RF pulse current corresponding to theLarmor frequency to the transmitting coil 6.

The receiving coil 8 is attached to the subject S placed in the imagingspace, and receives an MR signal emitted from the subject S by theinfluence of the RF magnetic field applied by the transmitting coil 6.The receiving coil 8 also outputs the received MR signal to the receiver9. For example, dedicated coils for the respective regions serving asimaging targets are used as the receiving coil 8. The dedicated coilsare, for example, a receiving coil for the abdomen, a receiving coil forthe head, and a receiving coil for the spine.

The receiver 9 generates MR signal data based on the MR signal receivedby the receiving coil 8. Specifically, the receiver 9 generates MRsignal data by converting the MR signal into a digital signal, andtransmits the generated MR signal data to a collector 24.

The embodiment illustrates an example of the case where the transmittingcoil 6 applies an RF magnetic field and the receiving coil 8 receives anMR signal, but the embodiments are not limited thereto. For example, thetransmitting coil 6 may further include a receiving function ofreceiving an MR signal, and the receiving coil 8 may further include atransmitting function of applying an RF magnetic field. In the casewhere the transmitting coil 6 includes a receiving function, thereceiver 9 generates MR signal data also from the MR signal received bythe transmitting coil 6. In the case where the receiving coil 8 includesa transmission function, the transmitter 7 supplies an RF pulse currentalso to the receiving coil 8.

The computer system 20 controls the whole MRI apparatus 100. Forexample, the computer system 20 includes an input unit 21, a display 22,a sequence controller 23, the collector 24, an image generator 25, astorage 26, and a system controller 27.

The input unit 21 receives inputs of various instructions and variouspieces of information from an operator. For example, the input unit 21is formed of an input device such as a keyboard, a mouse, a trackball, atouch panel, a button, and a switch.

The display 22 displays various pieces of information and variousimages. For example, the display 22 displays a graphical user interface(GUI) for receiving inputs of various instructions and various pieces ofinformation from the operator. For example, the display 22 also displaysan image generated by the image generator 25. For example, the display22 is formed of a display device such as a liquid crystal monitor, acathode-ray tube (CRT) monitor, and a touch panel.

The sequence controller 23 performs various scans. Specifically, thesequence controller 23 performs various scans by driving the gradientpower supply 3, the transmitter 7, and the receiver 9 based on sequenceexecution data transmitted from the system controller 27. The sequenceexecution data is information that defines a pulse sequence indicating aprocess for collecting MR signal data. Specifically, the sequenceexecution data is information that defines the timing at which thegradient power supply 3 supplies an electric current to the gradientcoil 2, the intensity of the supplied electric current, the timing atwhich the transmitter 7 transmits an RF pulse current to thetransmitting coil 6, the intensity of the transmitted RF pulse current,and the timing at which the receiver 9 detects an MR signal.

The collector 24 collects MR signal data generated by the receiver 9, asa result of performing various scans. Specifically, when the collector24 receives MR signal data from the receiver 9, the collector 24performs correction, such as averaging processing and phase correction,on the received MR signal data, and transmits the corrected MR signaldata to the image generator 25. The collector 24 also transmits thecollected image data to the computer system 20. A set of pieces of MRsignal data collected by the collector 24 are stored in the storage 26of the computer system 20, as data that forms a k space. In the set ofpieces of MR signal data, the pieces of MR signal data are arranged in atwo-dimensional manner or a three-dimensional manner in accordance withthe positional information provided by the readout gradient magneticfield, the phase encoding gradient magnetic field, and the slicegradient magnetic field.

The image generator 25 generates an image based on the MR signal datacollected by the collector 24. Specifically, when the image generator 25receives MR signal data from the collector 24, the image generator 25performs post processing on the received MR signal data, that is,reconstruction such as Fourier transform, to generate an image of thesubject S. The image generator 25 transmits data of the generated imageto the computer system 20.

The storage 26 stores various data. For example, the storage 26 storesMR image data collected by the sequence controller 23 and data of theimage generated by the image generator 25 for each subject S. Thestorage 26 also stores various computer programs and various data usedwhen the sequence controller 23, the collector 24, the image generator25, and the system controller 27 perform various pieces of processing.For example, the storage 26 is formed of a storage device such as arandom access memory (RAM), a read only memory (ROM), a flash memory, ahard disk, and an optical disk.

The system controller 27 controls the elements included in the MRIapparatus 100, to control the whole MRI apparatus 100. For example, thesystem controller 27 receives inputs of various imaging parameters fromthe operator via the input unit 21. The system controller 27 generatessequence execution data based on the received imaging parameters, andtransmits the generated sequence execution data to the sequencecontroller 23, to perform various scans.

As a result of performing various scans, the system controller 27 storesthe MR signal data transmitted from the collector 24 and the data of theimage transmitted from the image generator 25 in the storage 26. Thesystem controller 27 reads out an image required by the operator fromthe storage 26, and outputs the read image to the display 22. Inaddition, for example, the system controller 27 operates the couch 4, bycontrolling the couch controller 5 based on an instruction received fromthe operator via the input unit 21.

Among the elements described above, the sequence controller 23, thecollector 24, the image generator 25, and the system controller 27include, for example, processors such as central processing units (CPU)and micro processing units (MPU), memories, or electronic circuits suchas application specific integrated circuits (ASIC) and fieldprogrammable gate arrays (FPGA). In such a case, for example, therespective processors included in the sequence controller 23, thecollector 24, the image generator 25, and the system controller 27 readout from the storage 26 and perform computer programs that define theprocessing procedures of the processing to be performed by therespective elements, to perform the processing in accordance with theprocessing procedures.

The present embodiment illustrates an example of the case where each ofthe sequence controller 23, the collector 24, the image generator 25,and the system controller 27 includes a processor, but the embodimentsare not limited thereto. The structures of the sequence controller 23,the collector 24, the image generator 25, and the system controller 27may be distributed or integrated as appropriate. For example, the MRIapparatus 100 may include a processor, and the processor may perform theprocessing to be performed by the sequence controller 23, the collector24, the image generator 25, and the system controller 27. In addition,for example, the MRI apparatus 100 may include a plurality ofprocessors, and the processors may perform the processing to beperformed by the sequence controller 23, the collector 24, the imagegenerator 25, and the system controller 27, as appropriate in adistributed manner.

The configuration example of the MRI apparatus 100 according to thefirst embodiment has been explained above. With the structure describedabove, the MRI apparatus 100 calculates a feature value related to asignal value of the pixel for each of a plurality of pixels included inan image, corrects feature vectors of the respective pixels based on asensitivity of a receiving coil that receives an MR signal, and reducesnoise in the image based on distribution of the corrected featurevectors.

According to the first embodiment, the MRI apparatus 100 calculates afeature vector related to a signal value of the pixel for each of aplurality of pixels included in an image, and calculates a correctionmap based on information on distribution of noise generated in thepixels according to the imaging conditions. Thereafter, the MRIapparatus 100 corrects feature vectors of the respective pixels usingthe calculated correction map, and reduces noise in the image based ondistribution of the corrected feature vectors.

In the present embodiment, the feature value is indicated by a featurevector including a plurality of elements. However, the embodiments arenot limited thereto. For example, the feature value may be indicated byscalar.

For example, there is a method using a noise removal apparatus includingadjustment parameters according to the noise quantity of the input imageas a technique for removing noise in an image. This method requiresproviding the noise removal apparatus with a noise quantity fromoutside. In addition, for example, there is a method of calculating astandard deviation of temporal change of the signal intensity for eachof pixels of the frame, and determining an average of the calculatedstandard deviations of all the pixels as a magnitude of noise. Thismethod does not require a noise quantity provided from outside. However,because this technique uses an average of all the pixels, it may bedifficult to properly remove noise in all the pixels, for example, inthe case where difference in S/N exists among a plurality of pixelsincluded in the image, although the technique has no problem when thenoise quantity of the input image is uniform in the image.

Generally, MRI apparatuses are known to have an S/N that is non-uniformamong pixels, in the case of performing imaging with a plurality ofsmall receiving coils attached to the surface of the subject. Forexample, the S/N is high on the surface of the body close to thereceiving coils, and the S/N is low in the vicinity of the center of thebody that is distant from the receiving coils. In such a case, the abovetechniques cannot cope with the difference in S/N among pixels, andthere are cases where noise in a pixel having a noise quantity largerthan an estimated noise quantity cannot be entirely removed, or afeature of the signal of a pixel having a noise quantity smaller thanthe estimated noise quantity is lost.

By contrast, the MRI apparatus 100 according to the first embodimentcorrects the difference in S/N among pixels using information ondistribution of noise generated in the pixels according to the imagingconditions. With this structure, the MRI apparatus 100 can properlyremove noise in all the pixels even when the difference in S/N occursamong a plurality of pixels included in the image. This structuresuppresses loss of a feature of a signal in the image due to noiseremoval. More specifically, the MRI apparatus 100 according to the firstembodiment enables noise removal with which noise is properly removedfrom all the pixels and features of signals are hardly lost, throughenhanced accuracy of estimation of noise quantity enhanced by estimatinga noise quantity using a feature vector that reflects the imagingconditions as well as enhanced performance of the denoising processor.

FIG. 2 is a block diagram illustrating a configuration example offunctions included in the MRI apparatus 100 according to the firstembodiment. FIG. 2 illustrates the input unit 21, the display 22, thesequence controller 23, the storage 26, and the system controller 27,among the constituent elements included in the computer system 20illustrated in FIG. 1. For example, as illustrated in FIG. 2 the systemcontroller 27 includes a scan controller 27 a, a feature vectorcalculator 27 b, a correction map calculator 27 c, a corrector 27 d, amodel selector 27 e, and a denoising processor 27 f.

The scan controller 27 a receives inputs of various imaging parametersfrom the operator via the input unit 21. For example, the systemcontroller 27 displays the GUI for receiving inputs of the variousimaging parameters on the display 22, and receives inputs of the variousimaging parameters via the displayed GUI. The scan controller 27 agenerates sequence execution data based on the received imagingparameters, and transmits the generated sequence execution data to thesequence controller 23, to perform various scans.

The feature vector calculator 27 b calculates a feature vector relatedto a signal value of each of a plurality of pixels included in the imagegenerated based on the MR signal generated from the subject S. Thecorrection map calculator 27 c calculates a correction map based oninformation on distribution of noise generated in the pixels accordingto the imaging conditions. The corrector 27 d corrects the featurevectors of the respective pixels included in the image using thecorrection map generated by the correction map calculator 27 c.

The denoising processor 27 f reduces noise in the image based ondistribution of the feature vectors corrected by the corrector 27 d. Inthe present embodiment, as an example, the model selector 27 e selects anoise model from a plurality of noise models based on the distributionof the feature vectors corrected by the corrector 27 d. In the presentembodiment, the denoising processor 27 f reduces noise in the imageusing the noise model selected by the model selector 27 e.

FIG. 3 is a flowchart illustrating processing procedures of an imageprocessing method performed by the MRI apparatus 100 according to thefirst embodiment. The present embodiment illustrates an example of thecase where the MRI apparatus 100 calculates a correction map based oninformation on distribution of noise generated in the pixels accordingto the sensitivity of the receiving coil that receives a magneticresonance signal. Specifically, the present embodiment illustrates anexample of the case where the imaging condition described above is asensitivity of the receiving coil that receives a magnetic resonancesignal.

Specifically, in the present embodiment, the MRI apparatus 100photographs an image by high-speed imaging in which imaging is performedwith a plurality of receiving coils using difference in sensitivityamong the receiving coils, like SENSE disclosed in Non-Patent Document2. In the imaging, a sensitivity map that indicates distribution ofsensitivities of the receiving coils is collected in a preparatory scanthat is performed before a main scan that is mainly performed to collecta diagnostic image.

For example, as illustrated in FIG. 3, first, the scan controller 27 areceives setting of an imaging plan from the operator (Step S101).

For example, the scan controller 27 a holds in advance information on apulse sequence including initial set values of imaging parameters suchas repetition time (TR) and echo time (TE). For example, the scancontroller 27 a manages pulse sequence groups each including a pulsesequence for preparatory scans and a pulse sequence for main scans foreach of the imaging regions and imaging purposes. The scan controller 27a presents the pulse sequence groups to the operator for the respectiveimaging regions and imaging purposes via the GUI, and receives selectionand change of the pulse sequence group from the operator, to receive aninspection pulse sequence group executed in the inspection of the targetand settings of the imaging parameters.

Thereafter, when the scan controller 27 a receives an instruction tostart a preparatory scan from the operator (Yes at Step S102), the scancontroller 27 a starts execution of the preparatory scan based on theimaging plan that is set by the operator.

In the present embodiment, the scan controller 27 a performs a scan tocollect a sensitivity map, as a preparatory scan (Step S103).Thereafter, the collector 24 collects MR signal data obtained by thescan, and the image generator 25 generates a sensitivity map based onthe collected MR signal data. The preparatory scan may include a scanfor shimming, for example, and a scan for collecting a positioning imageto perform positioning of a diagnostic image.

Thereafter, when the scan controller 27 a receives an instruction tostart a main scan from the operator (Yes at Step S104), the scancontroller 27 a starts a main scan based on the imaging plan that is setby the operator.

Specifically, the scan controller 27 a performs a scan to collect adiagnostic image as a main scan (Step S105). Thereafter, the collector24 collects MR signal data obtained by the scan, and the image generator25 generates a diagnostic image based on the collected MR signal dataand the sensitivity map.

For example, the scan controller 27 a performs a plurality of scans tocollect a plurality of types of diagnostic images in the main scan. Forexample, in the case where the target region of the inspection is theheart, the scan controller 27 a performs a scan to collect a leftventricular short axis view, a scan to collect a left ventriculartwo-chamber long axis view, a scan to collect a left ventricularthree-chamber long axis view, and a scan to collect a left ventricularfour-chamber long axis view, in inspection of the cardiac function ofthe left ventricular system of the heart. The scan controller 27 a alsoperforms a scan to collect a right ventricular short axis view, a scanto collect a right ventricular two-chamber long axis view, a scan tocollect a right ventricular three-chamber long axis view, and a scan tocollect a right ventricular four-chamber long axis view, in inspectionof the cardiac function of the right ventricular system of the heart.

Thereafter, the feature vector calculator 27 b calculates featurevectors for the respective pixels included in the image generated by theimage generator 25 (Step S106). This explanation illustrates an examplein the case where the feature vector calculator 27 b uses atwo-dimensional still image as an input image.

Specifically, the feature vector calculator 27 b calculates afluctuation quantity of a signal value of the pixel, for each of thepixels included in the image, based on the signal value of the pixel andsignal values of other pixels located in positions spatially close tothe pixel, as an element of the feature vector.

For example, a feature vector v (x, y) includes a representative signalvalue v₀ (x, y) serving as a signal value that represents coordinates(x, y), and a fluctuation quantity v₁ (x, y) of the signal value in thecoordinates (x, y). For example, suppose that v₀ (x, y) is an average ofthe signal values, and v₁ (x, y) is a standard deviation of the signalvalue.

In such a case, for example, where (x, y) is coordinates indicating eachpixel included in the image; R is a neighborhood of (x, y) that is setby the operator in advance; N is the number of pixels included in R; ands (x, y) is a signal value, the feature vector v (x, y)=(v₀ (x, y), v₁(x, y)) is expressed by Expression (1) as follows.

$\begin{matrix}{\begin{pmatrix}{v_{0}\left( {x,y} \right)} \\{v_{1}\left( {x,y} \right)}\end{pmatrix} = \begin{pmatrix}\frac{\sum\limits_{{({x^{\prime},y^{\prime}})} \in R}{s\left( {x^{\prime},y^{\prime}} \right)}}{N} \\\sqrt{\frac{\sum\limits_{{({x^{\prime},y^{\prime}})} \in R}\left( {s\left( {x^{\prime},y^{\prime}} \right)}^{2} \right)}{N} - {v_{0}\left( {x,y} \right)}^{2}}\end{pmatrix}} & (1)\end{matrix}$

The neighborhood R is preferably set to include pixels corresponding tothe same body tissue therein as much as possible.

FIG. 4 is a diagram illustrating an example of the neighborhood R usedby the feature vector calculator 27 b according to the first embodiment.For example, as illustrated in FIG. 4, when noise is removed from thepixel 305, the feature vector calculator 27 b sets a block of 3×3including (x, y) as the center, like pixels 301 to 309 located aroundthe pixel 305, as the neighborhood R. In this case, N is 9.

If the block used as the neighborhood R is extended excessively, forexample, there can be cases where the feature vector is calculated froma signal string acquired across two types of organs, and thus theaccuracy of noise quantity estimation performed at the subsequent stagemay decrease. By contrast, if the block is narrowed down excessively,the accuracy of v₀ (x, y) and the accuracy of v₁ (x, y) may decrease,and thus the accuracy of the noise quantity estimation performed at thesubsequent stage may decrease. To prevent such problems, the operator ofthe MRI apparatus 100 selects the neighborhood R using the informationof the subject, for example.

With reference to FIG. 3 again, thereafter, the correction mapcalculator 27 c calculates a correction map to correct the featurevector v (x, y) based on the sensitivity map generated by the imagegenerator 25 (Step S107).

For example, suppose that the value of the coordinates in the correctionmap is M (x, y). For example, M (x, y) is a positive real number, andhas a large value when the S/N in the coordinates (x, y) is high. Thevalue of M (x, y) is set using the value that indicates the sensitivityof the receiving coil at each point of the sensitivity map. The value ofeach point in the sensitivity map has higher sensitivity as the point iscloser to the receiving coil. Specifically, the correction map has alarger value in a position with higher sensitivity of the receivingcoil.

Thereafter, the corrector 27 d corrects the feature vector v (x, y)calculated by the feature vector calculator 27 b using the correctionmap M (x, y) calculated by the correction map calculator 27 c (StepS108). In the following explanation, a corrected feature vector isreferred to as corrected feature vector v′ (x, y).

For example, the corrector 27 d performs correction on at least oneelement in the feature vector of each of a plurality of pixels includedin the image such that the element corresponding to a position having asmaller value in the correction map M (x, y) has a smaller value, andthe element corresponding to a position having a larger value in thecorrection map M (x, y) has a larger value. This correction unifies theconditions for observation of the fluctuation quantities of the signalvalues obtained from pixels having different S/N ratios.

For example, the corrected feature vector v′ (x, y) is expressed byExpression (2) as follows, when the corrected v₀ (x, y) is v₀′ (x, y)and the corrected v₁ (x, y) is v₁′ (x, y).

$\begin{matrix}{{v^{\prime}\left( {x,y} \right)} = {\begin{pmatrix}{v_{0}^{\prime}\left( {x,y} \right)} \\{v_{1}^{\prime}\left( {x,y} \right)}\end{pmatrix} = {{M\left( {x,y} \right)}\begin{pmatrix}{v_{0}\left( {x,y} \right)} \\{v_{1}\left( {x,y} \right)}\end{pmatrix}}}} & (2)\end{matrix}$

Thereafter, the model selector 27 e selects a noise model from aplurality of noise models stored in the storage 26 in advance using thecorrected feature vectors v′ (x, y) (Step S109).

For example, the model selector 27 e selects a noise model that mostapproximates to the data point group indicated by the corrected featurevectors v′ (x, y). For example, the noise model described herein outputsa noise quantity in response to an input signal value. The noise modeloutputs a smaller noise quantity as the input signal value is smaller,and outputs a larger noise quantity as the input signal value is larger.In addition, the output noise quantity of the noise model converges to afixed value as the input signal value increases.

FIG. 5 is a diagram illustrating an example of noise model selectionperformed by the model selector 27 e according to the first embodiment.FIG. 5 illustrates distribution of the corrected feature vectors v′ (x,y) obtained by the corrector 27 d. In FIG. 5, the horizontal axisindicates v₀′ (x, y) and the vertical axis indicates v₁′ (x, y).

For example, as illustrated in FIG. 5, suppose that a data point 401indicated by the corrected feature vector v′ (x, y) is obtained. Thedata point 401 corresponds to a corrected feature vector v′ (x, y).

For example, the storage 26 stores a plurality of noise models, each ofwhich outputs a value σ corresponding to the noise level, when a value scorresponding to a signal level is input. For example, the noise modelis expressed by Expression (3) as follows.

σ=f ₄₀₂(s)=a ₄₀₂ s+b ₄₀₂  (3)

The noise model is indicated by a straight line such as a broken line402 and a broken line 403 illustrated in FIG. 5, for example. Forexample, the model selector 27 e selects a noise model that runs closestto a data point group formed of a plurality of corrected featurevectors, from a plurality of straight-line noise models stored in thestorage 26. For example, when the storage 26 stores both of the noisemodel indicated by the broken line 402 and the noise model indicated bythe broken line 403 illustrated in FIG. 5, the model selector 27 eselects the noise model indicated by the broken line 402. This structureenables prediction of a noise quantity based on the S/N serving as thestandard from the signal value.

As a specific method, for example, the model selector 27 e may performthe Hough transform using the data point group illustrated in FIG. 5 asinput. The model selector 27 e may select a noise model having a minimumsum total of the absolute values of differences between estimated valuesσ of noise quantities estimated by using the representative signalvalues v₀′ (x, y) in the respective coordinates and the fluctuationquantities v₁′ (x, y) of the actually observed signal values.Specifically, the model selector 27 e selects a noise model F (s)expressed by Expression (4) as follows, when a plurality of noise modelsstored in the storage 26 are Fi (s).

$\begin{matrix}{{F(s)} = {\underset{i}{argmin}\left( {\sum\limits_{x,y}{{{v_{1}^{\prime}\left( {x,y} \right)} - {F_{i}\left( {v_{0}^{\prime}\left( {x,y} \right)} \right)}}}} \right)}} & (4)\end{matrix}$

With reference to FIG. 3 again, thereafter, the denoising processor 27 festimates noise quantities of the respective pixels included in theimage using the noise model F (s) selected by the model selector 27 e,and removes noise based on the obtained noise quantities (Step S110).

In the processing, for example, the denoising processor 27 f increasesthe intensity of noise removal, as the noise quantities output from thenoise model selected by the model selector 27 e increases.

Specifically, first, the denoising processor 27 f obtains F (s (x, y))using the signal value s (x, y) before noise removal. Thereafter, usingthe correction map M (x, y), the denoising processor 27 f performscorrection reverse to the correction performed by the corrector 27 d onthe v₁ (x, y), to obtain an estimated value σ (x, y) of the noisequantity in the coordinates (x, y).

Thereafter, the denoising processor 27 f increases the intensity of thedenoising processing for determining an output in the coordinates (x, y)as the estimated value σ (x, y) of the noise quantity increases, toobtain a signal value s′ (x, y).

The method for increasing the intensity of the denoising processingdepends on the noise removal method adopted by the denoising processor27 f. For example, as disclosed in Non-patent Document 1, the thresholdmay be set larger in the case of performing processing using a weightedaverage of signals only when difference of each of the signals from s(x, y) does not exceed the threshold. In the case of using filtering,the tap width of the filter may be increased, or a coefficient designedto further prevent high frequency from passing may be adopted.

Thereafter, the denoising processor 27 f displays a noise-removed imageon the display 22 (Step S111). The denoising processor 27 f may store anoise-removed image in the storage 26.

The processing procedure described above illustrates an example in thecase where scan to collect a sensitivity map is performed as preparatoryscan, but the embodiments are not limited thereto. For example, scan tocollect a sensitivity map may be performed during the main scan. Inaddition, a sensitivity map collected in another inspection on the samesubject may be used.

As described above, the MRI apparatus 100 according to the firstembodiment corrects signal values of the respective pixels usinginformation on distribution of noise generated in the pixels accordingto the sensitivity distribution of the receiving coil 8. Accordingly,the MRI apparatus 100 according to the first embodiment can properlyremove noise in all the pixels, even in the case where differences inS/N occur among a plurality of pixels included in the image due tonon-uniform sensitivity of the receiving coil 8. This structuresuppresses loss of features of signals in the image due to noiseremoval.

The present embodiment illustrates an example in the case of calculatinga correction map based on information on distribution of noise generatedin the pixels according to the sensitivity of the receiving coil, butthe imaging conditions are not limited to the sensitivity of thereceiving coil. For example, when differences in S/N occur among pixelsdue to another imaging condition, a correction map may be calculatedbased on information on noise distribution in accordance with theimaging condition.

In addition, the above embodiment may be modified to be carried out asin the following modification explained hereinafter.

First Modification

For example, the first embodiment described above illustrates an examplein the case where the feature vector calculator 27 b uses atwo-dimensional still image as an input image, but the dimensions of theinput image are not always limited to two dimensions. For example, theinput image may be three-dimensional volume data. Specifically, thedimensions of the input image may be increased, such as using a signalvalue s (x, y, z) of the input image and a correction map M (x, y, z).In addition, as another example, when a two-dimensional moving image isinput, s (x, y, z) and a correction map M (x, y) should be handled. Inthe case of inputting three-dimensional time-series data, a signal values (x, y, z, t) of the input image and a correction map M (x, y, z)should be handled.

Second Modification

In addition, for example, the shape of the neighborhood R used by thefeature vector calculator 27 b may be changed as desired according tothe information at the time of imaging. For example, when the scancontroller 27 a receives setting of the imaging plan from the operator,the scan controller 27 a further receives setting of the neighborhood R.The feature vector calculator 27 b calculates a feature vector using theneighborhood R received by the scan controller 27 a.

For example, when the MRI apparatus photographs a moving image of theheart, the subject can be regarded as hardly moving, because the doctorinstructs the subject (patient) not to move. In the case where thesubject can be regarded as hardly moving and the input image is atwo-dimensional moving image, for example, the feature vector calculator27 b may calculate a fluctuation quantity of a signal value of thepixel, as an element of the feature vector, for each of a plurality ofpixels included in the image, based on the signal value of the pixel andsignal values of other pixels located in positions temporally close tothe pixel.

FIG. 6 is a diagram illustrating a setting example of the neighborhood Raccording to the second modification. For example, as illustrated inFIG. 6, when a plurality of two-dimensional frames imaged at differenttimes t are input as the input image, the feature vector calculator 27 bmay set a range of pixels 501 to 503 located in the same position (x, y)in the respective frames, as the neighborhood R. Specifically, thefeature vector calculator 27 b may set the neighborhood R along the timedirection.

In addition, for example, when the same body tissue can be regarded ascontinuing in the direction of the backbone in three-dimensional volumedata obtained by imaging the lumbar vertebra and the like, a range of aplurality of pixels that are disposed along the direction of thebackbone may be set as the neighborhood R. Specifically, the featurevector calculator 27 b may set the neighborhood R along the shape of theregion to be imaged.

For example, as described above, in order to support the setting of animaging plan from the operator, when pulse sequence groups eachincluding a pulse sequence for preparatory scans and a pulse sequencefor main scans are managed for the respective imaging regions andimaging purposes, the neighborhood R may be set and managed according tothe imaging region and the imaging purpose. In such a case, theneighborhood R may be changeable via the GUI, or may be unchangeablewithout being displayed on the GUI.

For example, when the target region of the inspection is a region with ashape that widely changes with lapse of time such as the heart, thestandard deviation of the signal value may be prominently large forpixels of the portion with much movement. However, generally, becausethe area where the MR signals are collected is often set larger than thetarget region in consideration of aliasing artifact and the like,signals in portions with little movement are dominant in the wholecollected MR signals. With respect to this point, in the noise removalmethod described above, because a noise model that most approximates tothe feature vector data group, a noise model is selected based onfeature vectors of pixels in the portion with little movement as aresult, even when a signal value having a prominently large standarddeviation exists. For this reason, the noise quantity is estimated alsofor pixels of the portion with much movement, based on another portionwith little movement. Accordingly, the above method removes noise fromthe image without losing the feature of anatomical movement of thetarget region, even when the inspection target region is a region with ashape that widely changes with lapse of time such as the heart.

Third Modification

In addition, for example, the feature vector calculator 27 b does notnecessarily calculate feature vectors for all the pixels included in theimage. For example, the feature vector calculator 27 b may calculatefeature vectors only for pixels having x and y, both of which are evennumbers. As another example, when pixels that represent no body tissuesare recognized in advance, the feature vector calculator 27 b mayperform no calculation from the pixels. Specifically, the feature vectorcalculator 27 b may thin out pixels, to calculate feature vectors. Thisstructure reduces the calculation cost.

When the calculated value of the fluctuation quantity v₁ (x, y) of thesignal value is extremely large, the neighborhood R used for calculationof dispersion may include the boundary of the body tissue. For thisreason, for example, the corrector 27 d may use a threshold that is setby the operator in advance, to prevent use of the feature vectorscalculated by the feature vector calculator 27 b for such pixels.

Fourth Modification

In addition, for example, among the elements of the feature vector, amedian or a mode of the signal value may be used for v₀ (x, y), anddispersion or difference between the signal maximum value and theminimum value may be used for v₁ (x, y). For example, Rice distributionthat is known as a noise model of an image photographed by an MRIapparatus is known as becoming close to Gaussian distribution when thesignal level is high, and using the average and the standard deviationshould be theoretically compatible.

Fifth Modification

In addition, for example, the elements of the feature vector are notnecessarily limited to the following two elements: the signal value thatrepresents the coordinates and the fluctuation quantity in thecoordinates. For example, the feature vector calculator 27 b may includethe value of the sensitivity map used for calculation of a correctionmap in the elements of the feature vector.

Besides, for example, the feature vector calculator 27 b may include ageometry factor indicating accuracy of calculation of a signal value inimage reconstruction in the elements of the feature vector, when theinput image is obtained by high-speed imaging in which a plurality ofreceiving coils are used and imaging is performed using difference insensitivity among the receiving coils, such as SENSE disclosed inNon-patent Document 2.

When such expansion is performed, the noise model F (s) includesadditional terms of l that indicates a value of the sensitivity map, andg that indicates a geometry factor, as expressed in Expression (5) asfollows. In Expression (5), a, b, c, and d are real numbers.

F(s,l,g)=as+b+cl+dg  (5)

Sixth Modification

In addition, for example, the correction map M (x, y) may be calculatedso as to have a larger value at a position with lower accuracy ofcalculation of a signal value in image reconstruction when the inputimage is obtained by high-speed imaging in which a plurality ofreceiving coils are used and imaging is performed using difference insensitivity among the receiving coils. Non-patent Document 2 disclosesthat the S/N is a reciprocal number of the geometry factor. The geometryfactor does not affect the signal level of the image, unlike thesensitivity of the receiving coil. For this reason, for example,Expression (2) used for the feature vector may be changed such that therepresentative signal value v₀ (x, y) serving as a signal value thatrepresents the coordinates is not corrected, as expressed in Expression(6) as follows.

$\begin{matrix}{\begin{pmatrix}{v_{0}^{\prime}\left( {x,y} \right)} \\{v_{1}^{\prime}\left( {x,y} \right)}\end{pmatrix} = \begin{pmatrix}{v_{0}\left( {x,y} \right)} \\{{M\left( {x,y} \right)}{v_{1}\left( {x,y} \right)}}\end{pmatrix}} & (6)\end{matrix}$

The corrector 27 d may correct feature values of the respective pixels,further based on the geometry factor used in high-speed imaging. In sucha case, for example, the correction map calculator 27 c changes thevalue of the correction map M (x, y) to a value obtained by using twovalues, such as a value obtained by dividing the value of thesensitivity map of the receiving coil by the geometry factor, and avalue obtained by subtracting the geometry factor from the value of thesensitivity map of the receiving coil. In addition, the correction mapis not necessarily limited to one, but two correction maps may be usedtogether. For example, a correction map M₀ (x, y) resulting from datathat affects the signal level, and a correction map M₁ (x, y) resultingfrom data that does not affect the signal level may be prepared tocorrect the feature vectors as expressed in Expression (7) as follows.

$\begin{matrix}{\begin{pmatrix}{v_{0}^{\prime}\left( {x,y} \right)} \\{v_{1}^{\prime}\left( {x,y} \right)}\end{pmatrix} = {{M_{0}\left( {x,y} \right)}\begin{pmatrix}{v_{0}\left( {x,y} \right)} \\{{M_{1}\left( {x,y} \right)}{v_{1}\left( {x,y} \right)}}\end{pmatrix}}} & (7)\end{matrix}$

Seventh Modification

In addition, for example, the noise model F (s) is not necessarilylimited to the straight-line shape as expressed in Expression (3). Forexample, the noise model F (s) may be expressed by a special functionsuch as log (s) and s^(1/2), and a segmental polygonal line.

Eighth Modification

In addition, for example, the noise model F (s) does not necessarilyinclude a term corresponding to the signal value as expressed inExpression (3). For example, such a noise model is expressed as ahorizontal line like the broken line 403, in the example illustrated inFIG. 5. For example, Rice distribution that is known as a noise model ofan image photographed by an MRI apparatus is known as becoming close toGaussian distribution when the signal level is high, but the standarddeviation of Gaussian distribution does not depend on the signal level.In the case of using such a model, the input image may be binarized witha signal value, and only a bright portion may be used for feature vectorcalculation. Although this method should achieve high speed processingbecause the method reduces the calculation quantity of the modelselector 27 e, the method has a risk of reduction in noise removalperformance in the dark portion.

Ninth Modification

In addition, for example, the denoising processor 27 f may change thenoise removal method according to the characteristic of the targetsubject. For example, as described above, when a moving image of theheart is photographed by an MRI apparatus, the subject can be regardedas hardly moving, because the doctor instructs the subject (patient) notto move. In such a case, the denoising processor 27 f may remove noiseusing filtering in the time direction as disclosed in Non-patentDocument 1.

In addition, for example, the denoising processor 27 f may change thenoise removal method according to the imaging conditions. The imagingconditions described herein include, for example, the imaging region andthe imaging method. For example, the denoising processor 27 f performsnoise removal using filtering in the time direction, when the imagingregion is the heart. For example, the denoising processor 27 f performsnoise removal using filtering in the spatial direction when an imagingmethod using a contrast medium is performed.

Tenth Modification

Sixth modification illustrates an example in the case where the featurevalue is corrected further based on the geometry factor used inhigh-speed imaging, but the embodiments are not limited thereto. Forexample, according to the type of high-speed imaging, there are caseswhere the sensitivity of the image is corrected in the process ofreconstructing the image, without sensitivity correction using asensitivity map. In the case of using such high-speed imaging, thecorrector 27 d may correct the feature value based on the geometryfactor, without using a sensitivity map. In such a case, for example,the correction map calculator 27 c calculates a correction map based onthe geometry factor without using a sensitivity map.

In inspection of the heart, there are the cases of performing a scan tocollect a cine image (moving image) of the heart or a scan to collectvolume data of the heart, as a main scan, in one inspection, in additionto the scan to collect cross-sectional images such as a short axis viewand a long axis view. In such a case, the MRI apparatus 100 may switchthe noise removal method according to the type of images collected inthe respective scans. Specifically, the MRI apparatus 100 removes noisein a cross-sectional image by the method using a two-dimensional stillimage as the input image as explained in the first embodiment, removesnoise in a cine image by the method using a moving image as the inputimage as explained in the first modification, and removes noise involume data by the method using volume data as the input as explained inthe first modification.

Eleventh Modification

In addition, for example, when the input image is a moving image, noiseremoval methods may be different between a region including largemovement of the subject (a motion region) and a region including smallmovement of the subject (a static region).

In this case, the image generator 25 generates a moving image based onMR signal data. The denoising processor 27 f detects a motion region anda static region from the moving image generated by the image generator25, and reduces noise by different methods for each of the regions. Thestatic region referred to herein indicates a region including movementsmaller than that of the motion region, and is not always limited to acompletely static region.

For example, the denoising processor 27 f detects each pixel having alarger temporal fluctuation quantity of a signal value of the pixel inthe same position in a plurality of frames included in the moving imagethan a threshold based on a noise quantity obtained from the noisemodel, as a pixel of the motion region. The denoising processor 27 falso detects each pixel having a smaller or equal temporal fluctuationquantity of a signal value of the pixel in the same position in aplurality of frames included in the moving image than or to thethreshold based on the noise quantity obtained from the noise model, asa pixel of the static region.

FIG. 7 is a diagram illustrating an example of noise removal accordingto the eleventh modification. For example, the figure shown on the leftside of FIG. 7 illustrates a moving image 602 including a heart 601.

In the middle of FIG. 7, the horizontal axis indicates a value σ (x, y)of the noise quantity of each pixels, estimated by the above explainedmethod, and the vertical axis indicates a temporal fluctuation quantityv₁ (x, y) of a signal value. Every pixels in input image has one pointin the middle of FIG. 7 related to its σ (x, y) and v₁ (x, y). It'sexpected that a pixel in static region provides a point on a line 605 inthe middle of FIG. 7 whose slope is 1.0, because a difference between σ(x, y) and v₁ (x, y) becomes smaller. And it's also expected that apixel in moving region provides a point above line 605 (for examplepoint 607) because the v₁ (x, y) is affected by motion of the heart 601.

The denoising processor 27 f determines whether a pixel (x, y) belongsto the moving region or the static region by comparing the v₁ (x, y) andthe noise quantity σ (x, y) obtained from the noise model. For example,as illustrated on the middle of FIG. 7, a threshold 603 (for example aline whose slope is larger than 1.0) with respect to v₁ (x, y) is setaccording to a noise quantity σ (x, y) obtained from the noise model.For example, using a positive real number A and the noise quantity σ (x,y), the denoising processor 27 f determines pixels whose v₁ (x, y) isgreater than Aσ (x, y) belong to the moving region, and the othersbelong to the static region.

The denoising processor 27 f then detects each pixel having v₁ (x, y)larger than the threshold (line 603) as a pixel of the motion region,and detects each pixel having v₁ (x, y) equal to or smaller than thethreshold (line 603) as a pixel of the static region.

Hereby, in the moving image 601, for example, as illustrated on theright side of FIG. 7, a region 604 including the heart 601 and peripherythereof is detected as the motion region, and a region other than theregion 604 is detected as the static region.

Thereafter, the denoising processor 27 f reduces noise by differentmethods for each of the pixels included in the motion region and thepixels included in the static region. For example, the denoisingprocessor 27 f sets the intensity of noise removal for the pixelsincluded in the motion region to be lower than the intensity of noiseremoval for the pixels included in the static region.

FIG. 8 is a flowchart illustrating processing procedures of noiseremoval according to the eleventh modification. The processing at StepsS201 to S207 illustrated in FIG. 8 corresponds to the processing at StepS110 illustrated in FIG. 3. Specifically, in the present modification,the processing at Steps S201 to S207 illustrated in FIG. 8 is performedafter the processing similar to Steps S101 to S109 illustrated in FIG. 3is performed. Thereafter, processing similar to Step S110 illustrated inFIG. 3 is performed.

For example, as illustrated in FIG. 8, the denoising processor 27 festimates a noise quantity of each pixel included in each frame of themoving image using a noise model F (s) selected by the model selector 27e (Step S201). Specifically, the denoising processor 27 f determines anestimated value σ (x, y) of the noise quantity in the coordinates (x, y)using the noise model F (s) for each of the frames, as described above.

Thereafter, the denoising processor 27 f compares a numerical valueindicating the magnitude of the temporal fluctuation quantity of thesignal value with a threshold based on the estimated noise quantity, foreach pixel in the same position included in the frames (Step S202). Forexample, the fluctuation quantity v₁ (x, y) of the signal valueindicated in Expression (1) is used as the numerical value indicatingthe magnitude of the temporal fluctuation quantity of the signal value.For example, the threshold value is a value obtained by multiplying σ(x, y) by a magnification of a predetermined constant. For example, thethreshold is a value three times as large as σ (x, y).

Thereafter, the denoising processor 27 f determines a pixel having alarger numerical value indicating the magnitude of the fluctuationquantity of the signal value than the threshold value, as a pixel of themotion region (Yes at Step S202). By contrast, the denoising processor27 f determines a pixel having a smaller or equal numerical valueindicating the magnitude of the fluctuation quantity of the signal valuethan or to the threshold value, as a pixel of the static region (No atStep S202).

Thereafter, the denoising processor 27 f performs noise removal for eachof the pixels of the motion region and the pixels of the static region.In the removal, the denoising processor 27 f uses noise removal methodsthat are different between the motion region and the static region suchthat the intensity of noise removal for the pixels of the motion regionis lower than the intensity of noise removal for the pixels of thestatic region.

For example, the denoising processor 27 f performs noise removal byperforming processing using a weighted average of only signals having asmaller or equal difference from s (x, y) than or to a threshold value,as disclosed in Non-patent Literature 1. In this case, for example, thedenoising processor 27 f sets a threshold Tm used for noise removal forthe pixels of the motion region to be relatively lower than a thresholdTs used for noise removal for the pixels of the static region.

For example, the denoising processor 27 f sets the threshold Tm for themotion region to “Tm=Cm*σ (x, y)”, and sets the threshold Ts for thestatic region to “Ts=Cs*σ (x, y)”. Each of Cm and Cs is a positive realnumber, and Cm and Cs satisfy “Cm<Cs”.

In this manner, when the noise quantity is equal to or smaller than thethreshold Tm (No at Step S203), the denoising processor 27 f performsnoise removal with a relatively low intensity for the pixels of themotion region (Step S204), by performing weighted averaging of thesignal values. In addition, when the noise quantity is larger than thethreshold Tm (Yes at Step S203), the denoising processor 27 f performsnoise removal with a relatively medium intensity for the pixels of themotion region (Step S205), by performing ordinary averaging of thesignal values.

When the noise quantity is equal to or smaller than the threshold Ts (Noat Step S206), the denoising processor 27 f performs noise removal witha relatively medium intensity for the pixels of the static region (StepS205), by performing weighted averaging of the signal values. Inaddition, when the noise quantity is larger than the threshold Ts (Yesat Step S206), the denoising processor 27 f performs noise removal witha relatively high intensity for the pixels of the static region (StepS207), by performing ordinary averaging of the signal values.

As described above, the signal of the motion region becomes hard tochange due to noise removal, by setting the threshold Tm used for noiseremoval for the pixels of the motion region to be relatively lower thanthe threshold Ts used for noise removal for the pixels of the staticregion. This structure suppresses weakening of the signal in the motionregion due to noise removal.

Further, as described above, the denoising processor 27 f sets theintensity of noise removal for the pixels included in the motion regionto be lower than the intensity of noise removal for the pixels includedin the static region, when the pixels included in the motion region andthe pixels included in the static region have the equal noise quantityobtained from the noise model.

In addition, the intensity of noise removal for the motion region is notnecessarily less than the intensity of noise removal for the staticregion. For example, with respect to a point (x1, y1) on the motionregion and a point (x2, y2) on the static region, when σ (x1, y1) isequal to a (x2, y2), Cm is less than Cs and consequently Tm is less thanTs. However, when σ (x1, y1) is greater than Cs/Cm times of σ (x2, y2),Tm may be greater than Ts.

Further, the method for changing the intensity of noise removal betweenthe motion region and the static region is not limited to the abovemethod. For example, a method as disclosed in Non-patent Literature 3may be used. In the method, the gain of a high frequency included in thefilter for the motion region is set to be higher than the gain of a highfrequency included in the filter for the static region, intime-direction filtering.

Second Embodiment

The above first embodiment illustrates an embodiment of an MRIapparatus, but the embodiments are not limited thereto. For example, asimilar embodiment is applicable to an image processing apparatus thatis connected to an MRI apparatus via a network to communicate therewith.The following is explanation of an embodiment of an image processingapparatus that is a second embodiment.

FIG. 9 is a block diagram illustrating a configuration of an imageprocessing apparatus 30 according to the second embodiment. For example,as illustrated in FIG. 9, the image processing apparatus 30 is connectedto the MRI apparatus 100 via a network 40 to communicate therewith. Theimage processing apparatus 30 includes an input unit 31, a display 32, astorage 33, and a controller 34. For example, the controller 34 includesan image acquirer 34 a, a feature vector calculator 34 b, a correctionmap calculator 34 c, a corrector 34 d, a model selector 34 e, and adenoising processor 34 f.

The image acquirer 34 a performs communication with the MRI apparatus100 via the network 40, to acquire an image photographed by the MRIapparatus 100. For example, the image acquirer 34 a receives anoperation to designate an image serving as a processing target from theoperator via the input unit 31, and acquires the designated image and asensitivity map of the receiving coil at the time of photographing theimage from the MRI apparatus 100. The image acquirer 34 a stores theacquired image and the sensitivity map in the storage 33.

The feature vector calculator 34 b, the correction map calculator 34 c,the corrector 34 d, the model selector 34 e, and the denoising processor34 f have structures and functions similar to the feature vectorcalculator 27 b, the correction map calculator 27 c, the corrector 27 d,the model selector 27 e, and the denoising processor 27 f explained inthe first embodiment, respectively.

In the second embodiment, the feature vector calculator 34 b obtains animage generated by the image acquirer 34 a from the storage 33, andcalculates feature vectors for the respective pixels included in theobtained image. In the second embodiment, the denoising processor 34 fdisplays a noise-removed image on the display 32. The denoisingprocessor 34 f may store the noise-removed image in the storage 33.

The image acquirer 34 a may acquire sensitivity map MR signal data anddiagnostic MR signal data that are collected in the same inspection,instead of acquiring an image and a sensitivity map from the MRIapparatus 100. In such a case, for example, the controller 34 generatesa sensitivity map from the sensitivity map MR signal data and generatesan image from the diagnostic MR signal data, in the same manner as theimage generator 25 explained in the first embodiment, to store thegenerated sensitivity map and the image in the storage 33.

Among the elements described above, the controller 34 includes, forexample, a processor such as a central processing unit (CPU) and a microprocessing unit (MPU), a memory, or an electronic circuit such asapplication specific integrated circuits (ASIC) and a field programmablegate array (FPGA). In such a case, for example, the processor includedin the controller 34 reads and executes a computer program that providesthe processing procedure of the processing performed by the controller34 from the storage 33, to perform processing in accordance with theprocessing procedure.

The image processing apparatus 30 according to the second embodimentdescribed above can properly remove noise in all the pixels in an imagephotographed by the MRI apparatus 100, even when differences in S/Noccurs among a plurality of pixels. This structure suppresses loss of afeature of a signal in the image photographed by the MRI apparatus 100due to noise removal.

In the MRI apparatus or the image processing apparatus according to theabove embodiments, the computer program stored in the storage may beinstalled in each apparatus in advance, or distributed in a storagemedium such as a compact disc read only memory (CD-ROM) or via anetwork, to be installed in each apparatus, as appropriate. Otherwise,the program may be stored in a built-in or external hard disk in theapparatus, a memory, or a storage medium such as a compact discrecordable (CD-R), a compact disc rewritable (CD-RW), a digitalversatile disc random access memory (DVD-RAM), and a digital versatiledisc recordable (DVD-R), and read and executed by a processor includedin each apparatus, as appropriate.

At least one of the embodiments described above can properly removenoise in all the pixels even when differences in S/N occur among aplurality of pixels included in the image.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

What is claimed is:
 1. A magnetic resonance imaging apparatus,comprising: a magnetic resonance imaging (MRI) system including areceiving coil to receive magnetic resonance signals; and processingcircuitry configured to: generate an image based on the magneticresonance signals, the image including a plurality of pixels; calculatefeature values of respective pixels included in the image, based onsignal values of the plurality of pixels included in the image; correctthe calculated feature values, based on a fluctuation quantity of eachfeature value and a sensitivity of the receiving coil; and reduce noisein the image, based on distribution of the corrected feature values. 2.The apparatus according to claim 1, wherein the processing circuitry isconfigured to correct the feature values of the respective pixels,further based on a geometry factor used in high-speed imaging.
 3. Theapparatus according to claim 1, wherein the processing circuitry isconfigured to calculate the fluctuation quantity of each feature value,based on the signal value of the pixel and signal values of other pixelslocated in positions spatially or temporally close to the respectivepixel.
 4. The apparatus according to claim 1, wherein the processingcircuitry is configured to: calculate a correction map based on thesensitivity of the receiving coil, and correct the feature values of therespective pixels using the correction map.
 5. The apparatus accordingto claim 4, wherein a value of the correction map is calculated bydividing a value of the sensitivity of the receiving coil by a geometryfactor used in high-speed imaging.
 6. The apparatus according to claim4, wherein the correction map has a larger value in a position in whichthe receiving coil has higher sensitivity.
 7. The apparatus according toclaim 4, wherein the correction map has a larger value in a positionhaving lower calculation accuracy of a signal value in imagereconstruction, when the image is obtained by high-speed imaging inwhich a plurality of receiving coils are used and imaging is performedusing difference in sensitivity among the receiving coils.
 8. Theapparatus according to claim 4, wherein the processing circuitry isconfigured to correct at least one element of feature vectors of therespective pixels such that an element corresponding to a positionhaving a smaller value in the correction map has a smaller value and anelement corresponding to a position having a larger value in thecorrection map has a larger value.
 9. The apparatus according to claim4, wherein the processing circuitry is configured to decrease anintensity of noise removal as a value of the correction map increases.10. The apparatus according to claim 1, wherein each feature value isindicated by a feature vector including a plurality of elements.
 11. Theapparatus according to claim 10, wherein the feature vector includes astandard deviation of a respective signal value as one of the elements.12. The apparatus according to claim 1, wherein the processing circuitryis configured to: select a noise model from a plurality of noise models,based on the corrected feature values, and reduce the noise in the imageusing the selected noise model.
 13. The apparatus according to claim 12,wherein the noise model outputs a noise quantity for an input signalvalue, outputs a smaller noise quantity as the input signal value issmaller, and outputs a larger noise quantity as the input signal valueis larger, and the output noise quantity converges to a fixed value asthe input signal value increases.
 14. The apparatus according to claim12, wherein the processing circuitry is configured to select the noisemodel that most approximates to a data point group indicated by thecorrected feature values, from the plurality of noise models.
 15. Theapparatus according to claim 12, wherein the processing circuitry isconfigured to increase an intensity of noise removal as a noise quantitythat is output from the selected noise model increases.
 16. Theapparatus according to claim 12, wherein the processing circuitry isconfigured to: generate a moving image as a plurality of images, detecta motion region and a static region including movement smaller thanmovement in the motion region from the moving image, and reduce noise bydifferent methods for the motion region and the static region.
 17. Theapparatus according to claim 16, wherein the processing circuitry isconfigured to detect one of the plurality of pixels having a largertemporal fluctuation quantity of a signal value of the one of theplurality of pixels in an identical position in a plurality of framesincluded in the moving image than a threshold based on a noise quantityobtained from the noise model, as a pixel of the motion region.
 18. Theapparatus according to claim 16, wherein the processing circuitry isconfigured to set an intensity of noise removal for pixels included inthe motion region to be lower than an intensity of noise removal forpixels included in the static region, when the pixels included in themotion region and the pixels included in the static region have an equalnoise quantity obtained from the noise model.
 19. An image processingapparatus, comprising: a processor; and a memory that storesprocessor-executable instructions that, when executed by the processor,cause the processor to: acquire an image generated based on magneticresonance signals received by a receiving coil, the image including aplurality of pixels; calculate feature values of respective pixelsincluded in the image, based on signal values of the plurality of pixelsincluded in the image; correct the calculated feature values, based on afluctuation quantity of each feature value and a sensitivity of thereceiving coil; and reduce noise in the image, based on distribution ofthe corrected feature values.
 20. An image processing method,comprising: acquiring an image generated based on magnetic resonancesignals received by a receiving coil, the image including a plurality ofpixels; calculating feature values of respective pixels included in theimage, based on signal values of the plurality of pixels included in theimage; correcting the calculated feature values, based on a fluctuationquantity of each feature value and a sensitivity of the receiving coil;and reducing noise in the image, based on distribution of the correctedfeature values.